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6th IEEE Novel Intelligent and Leading Emerging Sciences Conference, NILES 2024

作者： Abd-Elaziz, Mariam M. Elshafie, Basma M. Abdullah, Hagar Gerges, Karen Y. Ghanem, Ingy A. Fouad, Khaled M. El-Ghamry, Amir Moawad, Ibrahim F. El-Metwally, Sara New Mansoura University Faculty of Computer Science and Engineering Dakahlia Egypt Mansoura University New Mansoura University Faculty of Engineering Computer and Communication Program Faculty of Computer Science and Engineering Egypt Benha University Information Systems Faculty of Computers and Artificial Intelligence Egypt Mansoura University New Mansoura University Faculty of Computer and Information Sciences Faculty of Computer Science and Engineering Egypt School of Engineering and Computer Science University of Hertfordshire Gaf Egypt Ain Shams University Faculty of Computer and Information Sciences Cairo Egypt College of Computer Science and Engineering Taibah University Department of Artificial Intelligence Medina42353 Saudi Arabia

ISBN: (纸本)9798350378511

Skin cancer's increasing incidence rates necessitate advanced diagnostic tools. This research uses MobileNet architecture to develop an enhanced system for skin cancer detection. MobileNet's efficient CNN architecture, combined with Keras for model lifecycle management and TensorFlow for real-time inference, forms the foundation of this study. Using 10,015 dermatoscopic pictures from seven different classifications of skin cancer in the HAM10000 dataset, the methodology includes several key stages: image preprocessing to correct illumination and adjust resolution, data augmentation to balance the dataset, and model training using Transfer Learning. The MobileNet model was trained over 50 epochs with a comprehensive architecture incorporating multiple specialized layers. By combining large-scale data analysis and adaptive learning, this methodology demonstrates The revolutionary possibilities of AI and ML in improving skin cancer diagnostics and public health outcomes. Keywords Skin Cancer Detection, Dermatoscopic Images, Machine Learning, Deep Learning © 2024 IEEE.

关键词： Adversarial machine learning

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Performance Evaluation of Multi-ONU Customers Access Network using Spectrum Slicing and Duplication Techniques 5

Performance Evaluation of Multi-ONU Customers Access Network...

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5th International Conference on Emerging Electrical Energy, Electronics and Computing Technologies, ICE4CT 2024

作者： Rahman, Juwairiyyah Abdul Mugunthan, Sivaguru A.L. Hassan, Hasliza Abu Muslim, Norliana Ab-Rahman, Mohammad Syuhaimi Department of Electrical Engineering Faculty of Engineering and Life Sciences Universiti Selangor Selangor Bestari Jaya45600 Malaysia Department of Computer and Communication Technology Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman Perak Kampar31900 Malaysia Department of Electrical Electronics and Systems Engineering Faculty of Engineering and Built Environment Universiti Kebangsaan Malaysia Malaysia Electrical and Computer Engineering Program Faculty of Engineering Sohar University PC:311 Sohar Oman

This study aims to increase the number of access users by limiting the sample size to 30 users while ensuring that every Optical Network Unit (ONU) receives data from the Optical Line Terminal (OLT). The proposed solution allows for the determination of power requirements at each ONU based on bus-star topologies, facilitating an increase in user capacity. The power received by each ONU is a critical parameter that system designers must consider when planning the implementation of Fiber to the Home (FTTH), as it impacts the output data in terms of Bit Error Rate (BER) and Signal Quality Factor (QF) for each user. Various parameters were evaluated to ensure that the enhanced network improves scalability while adhering to standard specifications. These parameters included the Q-factor, BER, eye diagram, user count, and received power. The study employed slicing and duplication techniques, utilizing Arrayed Waveguide Grating (AWG) for signal duplication and Wavelength Division Multiplexing for signal slicing. The simulations were conducted using a transmitter operating at a frequency of 1491 nm with a power output of 0 dBm, utilizing the latest version of OptiSystem 20 software. The results highlighted the system's capability to support a total of 73,728 users, meeting stringent performance criteria. Specifically, the system achieved a Q-factor greater than 6, indicating high signal quality, and a bit error rate (BER) lower than 1 × 10-9, ensuring reliable data transmission. For the sampled 30 users, the average Q factor was 8.1527, and the average BER was 5.3648 × 10^-11, with an output power level of -54.278 dBm. Despite an increase in users, the quality of the data supplied to clients remained unchanged. These outcomes underscore the robustness and efficiency of the design in delivering high-capacity, error-resilient optical communication. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Q factor measurement

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Optimal Resource Allocation for NOMA Wireless Networks

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computers, Materials & Continua 2023年第2期74卷 3249-3261页

作者： Fahad R.Albogamy M.A.Aiyashi Fazirul Hisyam Hashim Imran Khan Bong Jun Choi Turabah University College Computer Sciences ProgramTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Mathematics Jazan UniversityJazanP.O.Box 218Saudi Arabia Department of Computer and Communication Systems Engineering Faculty of EngineeringUniversiti Putra Malaysia(UPM)43400SerdangMalaysia Department of Electrical Engineering University of Engineering and Technology PeshawarP.O.Box 814Pakistan School of Computer Science and Engineering Soongsil UniversitySeoulKorea

The non-orthogonal multiple access(NOMA)method is a novel multiple access technique that aims to increase spectral efficiency(SE)and accommodate enormous user ***-user signals are superimposed and transmitted in the power domain at the transmitting end by actively implementing controllable interference information,and multi-user detection algorithms,such as successive interference cancellation(SIC),are performed at the receiving end to demodulate the necessary user *** its basic signal waveform,like LTE baseline,could be based on orthogonal frequency division multiple access(OFDMA)or discrete Fourier transform(DFT)-spread OFDM,NOMA superimposes numerous users in the power *** contrast to the orthogonal transmission method,the nonorthogonal method can achieve higher spectrum ***,it will increase the complexity of its *** power allocation techniques will have a direct impact on the system’s *** a result,in order to boost the system capacity,an efficient power allocation mechanism must be *** research developed an efficient technique based on conjugate gradient to solve the problem of downlink power *** major goal is to maximize the users’maximum weighted sum *** suggested algorithm’s most notable feature is that it converges to the global optimal *** compared to existing methods,simulation results reveal that the suggested technique has a better power allocation capability.

关键词： Non-orthogonalmultiple access wireless networks power domain 5G networks

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A Performance Study of Selected Machine Learning Techniques for Predicting Heart Diseases 2nd

A Performance Study of Selected Machine Learning Techniques ...

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2nd International Conference on Security and Information Technologies with AI, Internet Computing and Big-data Applications, SITAIBA 2023

作者： Olorunfemi, Blessing Oluwatobi Ogunde, Adewale Opeoluwa Adeniyi, Abidemi Emmanuel Awotunde, Joseph Bamidele Imoize, Agbotiname Lucky Li, Chun-Ta Faculty of Natural Sciences Department of Computer Science Redeemer’s University Osun Nigeria Department of Computer Science Bowen University Iwo Nigeria Faculty of Information and Communication Sciences Department of Computer Science University of Ilorin Ilorin240003 Nigeria Faculty of Engineering Department of Electrical and Electronics Engineering University of Lagos Lagos Akoka100213 Nigeria Bachelor’s Program of Artificial Intelligence and Information Security Fu Jen Catholic University New Taipei City24206 Taiwan

ISBN: (纸本)9789819777853

Heart Disease remains a leading cause of mortality worldwide. It alarmingly rises at a quick rate, making early heart disease prediction crucial for effective prevention and timely intervention. Heart disease diagnosis is a difficult process that requires technical skills and accuracy to complete. With improvements in technology, computing has lent its voice to simplify the diagnosis of various health problems. Machine learning uses past or existing history to predict future results. Various machine learning techniques have been developed over the years and used in predicting heart diseases with various levels of performance. Identifying the best-suited machine learning technique to use for prediction purposes can be a challenging task. This research work analyses the performance of seven (7) machine learning techniques, comprising AdaBoost Algorithm, KNN, Logistic Regression, Naïve Bayes Classifier, Random Forest, SVM, and XGBoost. The heart disease dataset was downloaded from the UCI repository and analysed using Python programming language in the Jupyter Notebook environment. A comparative analysis of the seven (7) techniques was performed based on Accuracy, Precision, and Recall. From the results obtained, KNN, Random Forest, and XGBoost showed superior performance over the others with an accuracy of 100%, AdaBoost Algorithm followed with an accuracy of 92.2%, SVM followed with an accuracy of 91.71%, Naïve Bayes Classifier followed with an accuracy of 88.29% while Logistic Regression has the least accuracy of 86.34%. KNN, RF, and XGBoost outperformed AdaBoost, SVN, and LR. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Evaluation of time-series database on microservice architecture for health monitoring system

Evaluation of time-series database on microservice architect...

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2022 International Symposium on Electronics and Smart Devices, ISESD 2022

作者： Simanjuntak, Eko Surantha, Nico Isa, Sani Muhamad Bina Nusantara University Binus Graduate Program-Master of Computer Science Computer Science Department Jakarta11480 Indonesia Tokyo City University Electronic and Communication Engineering Faculty of Engineering Department of Electrical Tokyo Japan

ISBN: (数字)9798350396607

ISBN: (纸本)9798350396607

Recently, the research on daily health monitoring using a wearable sensor has been continually evolving. In the future, when this system is actually implemented, a vast amount of data transmission will be conducted from multiple sensors to the cloud server. A system capable of transmitting, storing, processing, and visualizing massive volumes of data is necessary for health monitoring. This research aims to develop a health monitoring system that can accommodate a high amount of data with high performance. The proposed techniques include numerous modifications to system architecture and technology. In the system architecture, a multi-database is proposed to enhance the reliability of the system. As an enhancement to the technology aspect, a time series database and MQTT server are proposed. As the result, the proposed system achieves significantly better performance in terms of throughput, database-writing speed, and database-reading speed compared to the existing system. © 2022 IEEE.

关键词： Database systems

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NFT-Based Hydroponic Automated Control Using Adaptive Network-Based Fuzzy Inference System 2

NFT-Based Hydroponic Automated Control Using Adaptive Networ...

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2nd International Conference on Robotics, Automation and Artificial Intelligence, RAAI 2022

作者： Surantha, Nico Vincentdo, Vito Bina Nusantara University Binus Graduate Program - Computer Science Jakarta11480 Indonesia Tokyo City University Faculty of Scienc and Engineering Department of Electrical Electronics and Communication Engineering Japan

ISBN: (纸本)9781665459440

The rapid population growth and industrial development in developing countries harm the agricultural sector because many agricultural lands are converted into residential or industrial areas. Applying modern agriculture technologies such as hydroponic could help to overcome the problem. However, hydroponics requires special attention in adjusting the pH and nutrient levels to maximize plant growth, so an automated system is needed to manage the process. In this research, a smart hydroponic system is proposed by applying Adaptive Networkbased Fuzzy Inference System (ANFIS) and Internet-of-Things. The IoT system consists of sensor, actuator, and data processing layer is designed to monitor and control the condition of pH and nutrition of the observed plants. Then, the ANFIS algorithm is designed to control the level of pH and nutrition. The experiment results show that the system can automatically adjust the pH and nutrient levels to the expected range for growing plants, and the fuzzy controller made using ANFIS are more accurate and stable than the fuzzy controller made using Sugeno. This study shows that ANFIS has excellent performance when controlling multiple actuators, as long as the data set has great granularity and well defined. © 2022 IEEE.

关键词： Hydroponics

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The Contribution of Pancasila Education and Civic Education on Value Education for The Students During COVID 19 Pandemic in Digital Era

The Contribution of Pancasila Education and Civic Education ...

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2022 IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2022

作者： Benawa, Arcadius Lusia, Erma Alwino, Alfensius Irawan, Iwan Witono, Petrus Hepi Faculty of Economics and Communication Bina Nusantara University Character Building Development Center Mass Communication Program Communication Department Jakarta Indonesia Faculty of Economics and Communication Bina Nusantara University Tourism Department Jakarta Indonesia Bina Nusantara University School of Computer Science Character Building Development Center Computer Science Department Jakarta Indonesia Bina Nusantara University School of Industrial Engineering Industrial Engineering Department Jakarta Indonesia

ISBN: (数字)9781665470872

ISBN: (纸本)9781665470872

When all nations including Indonesia are hit by COVID-19 pandemic in era digital, we are compelled to conduct research with the main purpose, namely how much the contribution or influence of Pancasila Education and Civic Education on Value Education for students during the Covid-19 pandemic. The method used in this research is a quantitative method. The data was obtained by asking the students who were respondents to fill out a questionnaire. 150 students who have attended Pancasila and Citizenship courses were chosen by random sampling. Then the data obtained from the responses of the respondents were processed by the SPSS 22 program, and analyzed by path analysis. From the data processing and path analysis, the results of the research are obtained which prove that Pancasila Education has a significant contribution or influence on Value Education. Civic Education has no significant contribution or influence on Value Education. Based on these findings from this research, it is necessary to increase more intensive and serious efforts in delivering Pancasila Education, especially Civic Education in order to increase the contribution of both education to value education for the students, so that the students as the younger generation do not only have an understanding about Pancasila Education and Civic, but also be able to live the values, especially SPIRIT (Striving for the excellence, Perseverance, Integrity, Respect, Innovation, and Team Work) which are the core values at Bina Nusantara University. © 2022 IEEE.

关键词： Students

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Enhanced Plant Health Recognition via Humanoid Robot Advanced Sensing and AI

Enhanced Plant Health Recognition via Humanoid Robot Advance...

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Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

作者： Manar A. Ibrahim Haneen I. Fayez Salma M. Abdo Toqa E. Ghaly Ingy A. Ghanem Ibrahim Attiya Aya M. Ai-Zoghby Khaled M. Foad Faculty of Computer Science and Engineering Artificial Intelligence Science New Mansoura University Dakhlia Egypt Computer Science Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt Faculty of Engineering Computer and Communication program Faculty of Computer Science and Engineering Mansoura University New Mansoura University Dakhlia Egypt Department of Mathematics Faculty of Science Zagazig University Zagazig Egypt Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt Computer Science Department Faculty of Computers and Artificial lntelegence Damietta University Damietta Egypt

ISBN: (数字)9798350367775

ISBN: (纸本)9798350367782

Accurate and timely detection of plant diseases is crucial for protecting crop yields and promoting sustainable agriculture. This study introduces a deep learning-based approach for plant health detection by integrating a Convolutional Neural Network (CNN) with a Humanoid robot for real-time monitoring. The approach leverages advanced tools such as TensorFlow for model development, OpenCV for image processing, and YOLOv5 for object detection. A dataset comprising 1,530 images, labeled as “Healthy,” “Powdery,” and “Rust,” was used to train, validate, and test the model. Through pre-processing techniques like rescaling, data augmentation, and feature extraction, the model achieved impressive results, with a training accuracy of 98.4%, validation accuracy of 98.2%, and testing accuracy of 99.3%. This approach marks a significant improvement in precision agriculture, offering a scalable and highly accurate solution for early plant health detection.

关键词： YOLO Training Accuracy Humanoid robots Robot sensing systems Ubiquitous computing Real-time systems Sensors Convolutional neural networks Testing

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The Influence of Pancasila Education and Religion Education on Value Education for the Students during COVID-19 Pandemic (Case Study: Bina Nusantara University Students) 22

The Influence of Pancasila Education and Religion Education ...

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7th International Conference on Big Data and Computing, ICBDC 2022

作者： Benawa, Arcadius Lusia, Erma Alwino, Alfensius Irawan, Iwan Witono, Petrus Hepi Character Building Development Center Mass Communication Program Communication Department Faculty of Economics & Communication Bina Nusantara University Jakarta Indonesia Tourism Department Faculty of Economics & Communication Bina Nusantara University Jakarta Indonesia Character Building Development Center Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Character Building Development Center Industrial Engineering Department School of Industrial Engineering Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781450396097

It must be admitted that with the outbreak of the COVID-19 pandemic, conventional face-to-face learning has been disrupted. Luckily, we have to thank to advances in science and technology, so that the learning system does not have to stop, but can continue even though by online learning, either through zoom meeting or google meet or other online media. In such a context, this research was conducted with the main objective of finding out whether compulsory curriculum subjects, especially Pancasila Education and Religion Education, can still have a significant influence on character building through Value Education? Therefore, this simple research used quantitative methods by distributing questionnaires to 150 students as simple random sampling. From the data obtained, it was found the result that Pancasila Education and Religion Education still have a significant influence on Value Education which at Bina Nusantara University the values is focused to the value of SPIRIT (Striving for the excellent, Perseverance, Integrity, Respect, Innovation, and Teamwork). So, it can be concluded that the COVID-19 condition must not slacken the spirit to shape the character of students through Pancasila Education and Religion Education, so that core values of Bina Nusantara University remain embedded in students. © 2022 ACM.

关键词： Students

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Interconnected Through Robotic System in English Foreign Language Education 5th

Interconnected Through Robotic System in English Foreign Lan...

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5th International Conference on Advances in Information communication Technology and Computing, AICTC 2024

作者： Muthmainnah, M. de Barros Cardoso, Luís Miguel Oliveira Kaur, Dalwinder Burhamzah, Muftihaturrahmah Aeni, Nur Al Yakin, Ahmad Sharma, Aditi Teacher Training and Education Faculty Indonesian Language Study Program Universitas Al Asyariah Mandar Polewali Mandar Indonesia Portugal Department of Languages and Communication Sciences Polytechnic Institute of Portalegre Portalegre Portugal Manipal Globalnxt University Kuala Lumpur Malaysia English Study Program Universitas Negeri Makassar Makassar Indonesia Teacher Training and Education Faculty Civic Education Study Program Universitas Al Asyariah Mandar Polewali Mandar Indonesia Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International [Deemed University Pune India

ISBN: (纸本)9789819761050

Robots have become increasingly important in a variety of life domains as a result of the rapid advancement of technology. Additionally, robots are employed in education as teaching assistants in science, art, and language courses. This research aims to explore the potential of robotic systems as a multipurpose tool for increasing cultural awareness, language proficiency, and communicative competence in the GenAI era. Language games, conversation simulations, and real-time feedback mechanisms are just a few examples of interactive scenarios enabled by virtual robot systems or tutor robots that enable immersive language experiences that go beyond the limitations of conventional schooling. This research contributes knowledge that explores how AI can be used to personalize language teaching according to the needs and learning styles of each student in higher education. The total population of undergraduate students involved in this research was 350 for the 2023 academic year, with 24 students selected purposefully based on sample characteristics from this group who were exposed to using AI for one semester. The research method used is a mix method with quantitative analysis of the results of surveys and interviews with students who are known to have given positive responses and proposes a comprehensive strategy for using AI robot technology in EFL classrooms, which can significantly improve effective learning outcomes. The survey results also show that if educators have a solid understanding of the relationship between linguistic acquisition and technological advances, they will be better prepared to design engaging, individualized lessons that prepare their students to use the language effectively in real-world contexts. The robot-assisted language learning (RALL) approach used in this study, with the integration of robots in the classroom, has the potential to change the way people learn languages to make it more interesting, fun, and motivating, increase engagement

关键词： Students

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